Taking all these limitations into consideration, we propose a novel forgery localization method named Contrastive Forgery Localization Network or CFL-Net, based on recently proposed contrastive loss [1]}. Our method relies on the general assumption in underlying forged region localization that there remains a difference of feature statistics, i.e., color, intensity, noise, etc., between untampered region and manipulated region [2]}, irrespective of the forgery type. In this paper, we focus on leveraging this difference in the feature space to aid in image forgery localization via contrastive loss. Specifically, our model learns mapping into a feature space where the features between untampered and manipulated regions are well-separated and dispersed for each image. Thus, our method does not focus on specific forgery clues. Also, we calculate the contrastive loss for each sample. Hence, our method treats the forgery clues of each sample differently, which helps in generalization. Our main contributions are summarized as follows:
